A novel particle swarm optimization algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times

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Abstract


Much of the research on operations scheduling problems has either
ignored setup times or assumed that setup times on each machine are
independent of the job sequence. This paper deals with the hybrid flow
shop scheduling problems in which there are sequence dependent setup
times, commonly known as the SDST hybrid flow shops. This type of
production system is found in industries such as chemical, textile,
metallurgical, printed circuit board, and automobile manufacture. With
the increase in manufacturing complexity, conventional scheduling
techniques for generating a reasonable manufacturing schedule have
become ineffective. A particle swarm optimization algorithm can be used
to tackle complex problems and produce a reasonable manufacturing
schedule within an acceptable time. This paper describes a novel particle
swarm optimization algorithm approach to the scheduling of a SDST
hybrid flow shop. An overview of the hybrid flow shops and the basic
notions of a PSO are first presented. Subsequently, the details of a
NPSO approach are described and implemented. The results obtained
are compared with those computed by Random Key Genetic Algorithm
presented previously.

Keywords


Short-term scheduling; Hybrid flow shops; Sequence dependent setup times; Makespan; Particle Swarm Optimization

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